Web Content Summarization Using Social Bookmarking Service

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1 ISSN NII Technical Report Web Content Summarization Using Social Bookmarking Service Jaehui Park, Tomohiro Fukuhara, Ikki Ohmukai, and Hideaki Takeda NII E Apr. 2008

2 Jaehui Park School of Computer Science and Engineering Seoul National University Seoul , Republic of Korea Web Content Summarization Using Social Bookmarking Service Tomohiro Fukuhara RACE (Research into Artifacts, Center for Engineering) The University of Tokyo, Kashiwa Chiba , Japan Ikki Ohmukai 1, Hideki Takeda 2 1 Digital Content and Media Science Reserch Division, 2 Principles of Informatics Research Division National Institute of Informatics, Tokyo , Japan ABSTRACT In this paper, we propose a novel web content summarization method that creates a text by exploiting user feedback (comments, annotations etc.) in the social bookmarking service. We first analyze the feasibility to utilize user feedback in the summarization, and then demonstrate how the social which best represents the topics of the web content can be generated. Performance evaluations on our method are conducted by comparing its output with the manual summaries generated by human evaluators. Categories and Subject Descriptors H.3.1 [Content Analysis and Indexing]: Abstracting methods; H.3.3 [Information Search and Retrieval]: Information filtering, Selection process from comments. Furthermore, we demonstrate how this summarization technique can generate that are of similar quality that those produced by human. 2. BACKGROUND In this paper, we suggest an idea for summarization that is inspired by the recent emergence of so-called social services. By encouraging users to submit a note or a tag, they can express their views of the contents. The point of all this approach is based today s active user role in web environment. There are so many emerging services that allow users to play more active roles, eventually enriching or enhancing the original content. General Terms Experimentation. Keywords Social summarization, Social bookmarking service, user feedback 1. INTRODUCTION Weblogs (Blogs) provide individual users with an easy way to publish online and others to comment on that. This characteristic of blogs makes them quite different from other internet contents. This could be used as a medium for social conversation. Permitting readers to participate the conversation, the blog entry can have a chance to aggregate related information from so-called social knowledge. Recently, many of internet services such as social bookmarking allow users to gather blog entries and to comment on it. As these services have matured and grown more popular, social knowledge which we can exploit will become richen and qualified. In spite of blog entries containing comments, existing studies are largely focused on the post only. In this paper, we describe a summarization technique using user comments. First, we analyze comments of blog post in social bookmark service. The focus of our analysis is on the finding features for blog summarization Figure 1. The Concept of Social Summarization Based on user s active role, enriched data from the social services such as social bookmark services can be exploited for social summarization. Social summarization focuses on human s act toward information. Figure 1 shows the concept of social summarization. 3. PREVIOUS WORK Much work has been done on Weblog Contents summarization. In this section we give a brief overview of Nevertheless, very few studies on blog comments and blog post summarization have been reported [1][2]. The most recent one is

3 [2]. In this approach aim to extract representative sentences from a blog post that best represent the topics discussed among its comments. This paper shows information hidden in comments. 4. ANALYSIS OF COMMENT Social bookmark services run as a tool for gathering various users comments. Here we choose representative for this: Del.icio.us and Digg [3][4]. Del.icio.us is a social bookmark service for storing and sharing web bookmarks. This is one the most popular social bookmark services. Users can tag a bookmark or put a little note on it. Digg is a community-based news article site. It combines a social bookmark service and blogging service. Once one user post a bookmark, other users can simply rate (called diggs) or comment on the article. We examined user note in Del.icio.us and comment in Digg. In spite that both have characteristics of their own service, we just focused on the contents of a bookmark and comments on it. Table 1. Statistics of Experiment Data Parameters Value Number of participant 10 Number of Articles 70 Number of Comments 2504 Data Type Text, Video, Image Etc. Spam, slang, non-english, joke, sarcasm, all including irrelevant information Categorizing comments, we found some patterns according to social bookmark services: Del.icio.us and Digg, Figure 2 shows the different distribution of comment categories between them. Figure 2. Number of comments in Del.icio.us and Digg Firstly, for understanding patterns in comments, we collect 10 participants who are graduate students majoring in computer science. They have read comments on a number of articles during a week, and 70 articles and 2504 comments were gathered. Table 1 shows the statistics of experiment data. Gathered comments are manually categorized into 5 groups: 4.1 Categories Summary A main summarization feature for this work. Mostly, a collection of important sentences or sentence fragments from the original article are classified into this category. And paraphrase for the source content to provide a more concise representation is included Additional Information Related information from the external sources such as related links, quotations, comparisons with another post, and so on Impression An expression for describing user s sentiment when he sees the article such as Awesome, nice background design. Most of them includes adjective phrase Opinion An explicit opinion against the article expressing a judgment or an assessment. This information could be used for enriching the original article in social ways. In a sense, getting the point is complicate work even for human. Figure 3. Pie Chart for Del.icio.us Like Figure 3, in Del.icio.us, over 62% of comments are classified into category Summary. In 14.6% of Etc, there are many comments written in other languages like Japanese. If we could understand other languages, the portion for Summary might be bigger. This result shows that users of Del.icio.us have a tendency to summarize the contents of the bookmark. In user page there are all the bookmarks he/she made and user notes for each. When users create a bookmark, they put a user note on it. This page can be exposed to other users, but cannot be altered or added by them. This means that users have no place to discuss with other users, but more for their own uses such as summarizing original post concisely. Based on this analysis, comments in Del.icio.us are thought to be suitable for summarization.

4 ranking function. And the English stop word dictionary is added to filter out word has no meaning such as is, the and so on [7]. These improve the original word frequency algorithm performance notably. Using this customized tf-idf algorithm we calculate weight of each word appeared in comment list. After that, we sum this weight up for constructing one complete sentence. Here we see one of the examples. Figure 4. Pie Chart for Digg Like Figure 4, in Digg, about 40% of their comments were cannot be processed. Most of them were jokes and sarcasm. However, comments classified into category Opinion occupy over 25%. This includes useful information supports the original one. This is because bookmarked (called dugg) web contents exposed in the page which every user can add comments. Everyone express their thought by comments. Some comments like a shrewd criticism or a detail feedback are regarded as a social knowledge of good quality. In Digg we found twice more the number of comments than in Del.icio.us. However, because of good quality of summaries in del.icio.us, we made use of comments in del.icio.us for our experiments. Based on our comment analysis, we expect that a comment list in the social bookmark services could be very useful for blog summarization. 5. EXPERIMENTS We investigate how comments in social bookmark services are contribute to the blog summarization. The comment list are collected from Del.icio.us Today s popular items. On the average, there are 25 comments for one bookmark and over 62% of comments are good summaries for the bookmarked page. The final goal of this experiment is finding good set of sentences from comment list on the assumption that most of comments in del.icio.us have good. Firstly, we extract words from comment list based on statistical natural language processing algorithm. In a collection of comment, we apply conventional tf-idf weighting algorithm to each word in a comment. The weight increases proportionally to the number of times a word appears in the comment but is offset by the frequency of the word in the collection. This weighting scheme is often used by information retrieval field such as search engine [5]. But conventional tf-idf algorithm work at poor performance in our experiment. This is because comments in the same collection mostly have a same set of topics. The rank of a word relevant to a small set of topics falls same as a rank of the word can be used when talking about almost any topic or any context. Hence, we employ POS (Part of speech) tagger developed by The Stanford Natural Language Processing Group [6]. This tool reads text and assign parts of speech to each word, such as noun, verb, adjective, etc. Based on the comment analysis before, we determine that noun, verb, adjective parts will be gained more weights than others. This selection policy is added into our Figure 5. Del.icio.us example page Figure 5 shows comment list (user note in Del.icio.us) page for bookmark which links to blog post titled Why I m excited about the Google Social Graph API Bokardo. In this comment list, there are all 39 sentences. Sentences are classified into categories as follows, Table 2. Table 2. Comments classfied into 5 categories Summary 20 Additional Information 3 Impression 2 Opinion 5 Etc. 9 Knowing that these examples have about 50% summarization features, we run our algorithm. Among the weighted result sentences, we choose top-k sentences in Table 3. Table 3. Top 10 sentences extracted and weight category sentence weight The Google Social Graph API is a new programming API that allows developers to expose social relationships embedded in web sites What if you want to combine your Amazon book history with your friends lists at Facebook so that you can see what your friends are reading

5 additional information Etc. The Social Graph API helps solve this silos of information problem by allowing people to write software that understands who your friends are The truth is that Facebook Amazon or even Twitter never had a good glimpse of my true social network anyway Do you ever feel like your personal information is spread across the web in a whole bunch of separate places I never gave Facebook my list they don t know anything about my blog and I m going t What does this mean for regular folks like you and me It does this by reading your web site or blog and making connections between the social profiles you have While Google is providing the API nobody is dependent on them for creating or storing our relationships The social relationships that the API exposes are encoded in regular old HTML using the XFN and FOAF formats We perform the experiment on 20 bookmark pages which includes average 18 comments each. The average precision at 20 is 0.54 and the average recall is (at a given cu-off rank, the recall value does not really matter.) 6. DISCUSSION For the example above, the precision at 10 is the value of 0.9 and recall is (When we retrieve more comments to 20 sentences, the precision is 0.7 and recall is 0.7.) This result only makes sense on the assumption that there are enough (over a half) sentences for summarization in the comment list. We call this summarization feature in the social services like social bookmark service. In out experiment, there are over 51% sentences. If there are few related information, this approach may perform poor. Besides, among the low ranked sentences, though we can find a good and more concise one, the algorithm cannot pick that up. The sentence Google Social Graph API is weighted We think this one is one of the best summaries. There are tendency in our sentence constructing technique that the longer sentence gets the more weight from extracted word. On the other hand, in the example of blog titled What is Web 2.0 Tim Oreilly, the good is made of common word such as Web, 2.0 and so on. But to find out this good : This article is an attempt to clarify just what we mean by Web 2.0, considering Web 2.0 rather than Web and 2.0 is one of the effective ways. This will be resolved by introducing word dictionary or the grammatical solution such as linguistic tree constructing. In this paper, we have described an approach to blog summarization, which we call social summarization that uses the comment list in the social bookmark service such as del.icio.us. In turn, we have presented the experiment about measuring performance for this approach. This result clearly highlights the potential benefits of this summarization technique, which is seen to produce socialized information from many users, closely related to a human generated gold-standard. In our experiments, sentence scores are sometimes very high because several words are repeated. Future direction of this approach is the consideration of sentence evaluation factors such as length, word s position and so on. In addition word extraction algorithm should be refined for more comprehensive study on comment pattern. 7. CONCLUSION This paper presents a blog summarization technique using comments appeared in the social bookmark service. Based on an analysis of the comment classification in the social bookmark services, we found that users have propensity to make a for their bookmark contents. Our proposed solution finds representative words in comments set by term frequency weighting, grammatical tagging, and stop-word filtering. Using generated from the representative words, we evaluated performance of our work. 8. REFERENCES [1] Gilad Mishne and Natalie Glance Leave a Reply: An Analysis of Weblog Comments. In Proceedings of the 3 rd Annual Workshop on the Weblogging Ecosystem (The Edinburgh, The Scotland, April 23-26, 2006) [2] Meishan Hu, Aixin Sun and Ee-Peng Lim Comments- Oriented blog Summarization by Sentence Extraction. In Proceedings of the 16 th Conference on Information and Knowledge Management (Lisboa, Portugal, November 6-9, 2007) [3] Del.icio.us [4] Digg [5] Christopher D. manning and Hinrich Schutze Foundations of Statistical Natural Language Processing, The MIT Press. [6] The Stanford POS Tagger [7] A list of English stop words

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